from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge
from sklearn.metrics import mean_squared_error

def linear_egression_demo():
    """
    正规方程的优化方法对波士顿房价进行预测
    :return:
    """
    # 1.获取数据
    boston = load_boston()
    print("特征数量：\n", boston.data.shape)
    # 2.划分数据集
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=22)
    # 3.标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4.预估器
    estimator = LinearRegression()
    estimator.fit(x_train, y_train)
    # 5.得出模型
    print("正规方程权重值为：\n", estimator.coef_)
    print("正规方程偏置为：\n", estimator.intercept_)
    # 6.模型评估
    y_predict = estimator.predict(x_test)
    error = mean_squared_error(y_test, y_predict)
    print("预测房价：\n", y_predict)
    print("正规方程-均方误差为：\n", error)

    return None

def sGD_regressor_demo():
    """
    梯度下降的优化方法对波士顿房价进行预测
    :return:
    """
    # 1.获取数据
    boston = load_boston()
    # 2.划分数据集
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=22)
    # 3.标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4.预估器
    estimator = SGDRegressor()
    estimator.fit(x_train, y_train)
    # 5.得出模型
    print("梯度下降重值为：\n", estimator.coef_)
    print("梯度下降偏置为：\n", estimator.intercept_)
    # 6.模型评估
    y_predict = estimator.predict(x_test)
    error = mean_squared_error(y_test, y_predict)
    print("预测房价：\n", y_predict)
    print("梯度下降-均方误差为：\n", error)

    return None


def ridge_demo():
    """
    岭回归的优化方法对波士顿房价进行预测
    :return:
    """
    # 1.获取数据
    boston = load_boston()
    # 2.划分数据集
    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=22)
    # 3.标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4.预估器
    estimator = Ridge()
    estimator.fit(x_train, y_train)
    # 5.得出模型
    print("岭回归重值为：\n", estimator.coef_)
    print("岭回归偏置为：\n", estimator.intercept_)
    # 6.模型评估
    y_predict = estimator.predict(x_test)
    error = mean_squared_error(y_test, y_predict)
    print("预测房价：\n", y_predict)
    print("岭回归-均方误差为：\n", error)

    return None

if __name__ == "__main__":
    # 代码15：正规方程的优化方法对波士顿房价进行预测
    linear_egression_demo()
    # 代码16：梯度下降的优化方法对波士顿房价进行预测
    sGD_regressor_demo()
    # 代码17：岭回归的优化方法对波士顿房价进行预测
    ridge_demo()